Signature changes and modifications within security systems don't solve the issue, nor do alternative approaches such as analysing every bit of traffic or every file to make sure it is safe. NGFWs can often use heuristics to detect modified malware, there always has to be a Victim 0 - a first organisation or person who experiences the attack. Inline ML-powered prevention on the NGFWĪttackers will often use existing attack methods and modify them so they can slip past traditional signature-based security systems. Palo Alto Networks incorporates ML into its security solutions, including next-generation firewalls (NGFWs) to create a truly proactive solution for network security.Īccording to Palo Alto Networks, there are four key elements of an ML-powered next-generation firewall (NGFW):ġ. Palo Alto Networks believes it's time for security administrators to fight attackers - and the automated tools attackers use - with ML. This is where ML really makes a difference. Security administrators cannot keep pace with such a rapidly changing threat landscape, let alone how many devices (and what those devices are doing) on their networks. They use rigid, set lists of rules to keep bad traffic and requests from making their way into a company's network.īut there's a problem: The IT environment - within an organisation and within the threat actor's toolbox - changes far too rapidly for a traditional security solution - firewall or otherwise - to keep up with. Take the traditional firewall, for example. Further, organisations that use ML to support their security posture have an extra edge over those that don't. Machine learning has been a game-changer for almost every aspect of technology, including cybersecurity.įrom cloud and software-based threat prevention to firmware in millions of security appliances, machine learning (ML) powers cybersecurity in millions of organisations around the world.
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